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My project is part of the E-MUSE Network, which aims to develop innovative modelling methodologies to improve knowledge about complex biological systems and to control and/or predict their evolution by combining artificial intelligence and systems biology, using cheese ripening as a case study.
In this project, we will develop tools and methods to integrate dynamic multi-level omics data with genome-scale mechanistic models in the context of microbial communities. With partners in the E-MUSE Network we will explore:
(1) the use of machine-learning approaches for feature selection for model development and prioritisation;
(2) conversely the use of the mechanistic models and Individual-based Model as prior knowledge to constrain the search space in data-driven approaches.
We will integrate the data into dynamic constraint-based ecosystem models, and apply the tools and methods to the biological study cases, i.e. the cheese ripening case and the growth physiology of associated microbial species.